{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"CNN2a.ipynb","provenance":[{"file_id":"11CKvMWbYn2HYpLAfu2xRhNmw9HDnkG5B","timestamp":1575826262900},{"file_id":"1nbofAi2dLnhJvkcLl1Xag7I1vx4MPR2s","timestamp":1575818931262},{"file_id":"1tTQLWB1ve20LC-Cxir5CrLKQ_vDyzeI2","timestamp":1575815207334}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"LAgCWuBBRPSJ","colab_type":"text"},"source":["# CNN2a SETTINGS\n","\n","SUBJECT'S DATA SELECTION:                                                                                                  \n","- **A**: Signal samples: 7794, EEG channels: 64, Testset letters: 100                                                           \n","- **B**: Signal samples: 7794, EEG channels: 64, Testset letters: 100 "]},{"cell_type":"code","metadata":{"id":"9nw7AE3rLWlk","colab_type":"code","outputId":"e0effdc6-0da1-4a1a-fe03-fe3a94f4abf8","executionInfo":{"status":"ok","timestamp":1576425785236,"user_tz":-60,"elapsed":5307,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":200}},"source":["import matplotlib.pyplot as plt\n","import seaborn as sns\n","import pandas as pd\n","import numpy as np     \n","import random           \n","from scipy.io import loadmat\n","from scipy import signal\n","from google.colab import drive\n","import warnings\n","import string\n","import os\n","\n","from keras.layers import *\n","from keras.models import Model\n","from keras import backend as K\n","from keras import Sequential\n","from keras.callbacks import ModelCheckpoint\n","from keras.callbacks import EarlyStopping\n","from sklearn.metrics import confusion_matrix\n","from sklearn.preprocessing import scale\n","from sklearn.preprocessing import MinMaxScaler\n","\n","# Install mne library for topoplot\n","!pip install mne\n","import mne\n","\n","warnings.filterwarnings('ignore')\n","drive.mount('/content/drive')\n","\n","####################### SETTINGS ########################\n","# Set this variable with the desidered subject's letter #\n","SUBJECT_SELECTED = \"B\"                                  #\n","#########################################################\n","\n","subject_names = [\"A\", \"B\"]\n","\n","# Check for errors in the settings\n","if SUBJECT_SELECTED not in subject_names:\n","    raise ValueError(\"SUBJECT_SELECTED value {} is invalid.\\nPlease enter one of the following parameters {}\".format(SUBJECT_SELECTED, subject_names))\n","\n","\n","# Google drive data paths\n","MODEL_LOCATIONS_FILE_PATH = 'drive/My Drive/AY1920_DT_P300_SPELLER_03/Trained_models/CNN2a/' + SUBJECT_SELECTED\n","SUBJECT_TRAIN_FILE_PATH = 'drive/My Drive/AY1920_DT_P300_SPELLER_03/Dataset/Subject_' + SUBJECT_SELECTED + '_Train.mat'\n","SUBJECT_TEST_FILE_PATH = 'drive/My Drive/AY1920_DT_P300_SPELLER_03/Dataset/Subject_' + SUBJECT_SELECTED + '_Test.mat'\n","CHANNEL_LOCATIONS_FILE_PATH = 'drive/My Drive/AY1920_DT_P300_SPELLER_03/Dataset/channels.csv'\n","CHANNEL_COORD = 'drive/My Drive/AY1920_DT_P300_SPELLER_03/Dataset/coordinates.csv'\n","\n","# Channel selection\n","CHANNELS = [10, 33, 48, 50, 52, 55, 59, 61]            "],"execution_count":0,"outputs":[{"output_type":"stream","text":["Using TensorFlow backend.\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["<p style=\"color: red;\">\n","The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n","We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n","or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n","<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Requirement already satisfied: mne in /usr/local/lib/python3.6/dist-packages (0.19.2)\n","Requirement already satisfied: scipy>=0.17.1 in /usr/local/lib/python3.6/dist-packages (from mne) (1.3.3)\n","Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.6/dist-packages (from mne) (1.17.4)\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/numba/decorators.py:146: RuntimeWarning: Caching is not available when the 'parallel' target is in use. Caching is now being disabled to allow execution to continue.\n","  warnings.warn(msg, RuntimeWarning)\n"],"name":"stderr"},{"output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"CVrJuHCMFCSl","colab_type":"text"},"source":["# Training set processing\n","\n","1.   Application of bandpass filter **(0.1-20Hz)**;\n","2.   Down-sampling signals from 240Hz to 120Hz;\n","3.   Obtain windows of 650ms at the start of every flashing (175ms)\n","4.   Normalization of samples in each window: **Zi = (Xi - mu) / sigma**;\n","5.   Reshape each window to be a 3D tensor with dimensions: **(N_SAMPLES, 78, 8)**;\n","6.   Obtain **(noP300 / P300)** class ratio to balance out dataset during training;\n","\n","\n","\n","\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"78aU0w6-Lp0y","colab_type":"code","outputId":"ff8a0f22-2471-4da8-a514-8c73a8f1fa28","executionInfo":{"status":"ok","timestamp":1576425785491,"user_tz":-60,"elapsed":5544,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":134}},"source":["if not os.path.exists(SUBJECT_TRAIN_FILE_PATH):\n","    print(\"Missing file: {}\".format(SUBJECT_TRAIN_FILE_PATH))\n","else:\n","    # Load the required data\n","    data = loadmat(SUBJECT_TRAIN_FILE_PATH)\n","    # Get the variables of interest from the loaded dictionary\n","    signals = data['Signal']\n","    # Take only the 8 selected channels of the singal [Fz, Cz, Pz, P3, P4, Po7, Po8, Oz]\n","    signals = signals[:, :, CHANNELS]\n","    \n","    flashing = data['Flashing']\n","    stimulus = data['StimulusType']\n","    word = data['TargetChar']\n","    \n","    SAMPLING_FREQUENCY = 240\n","    # From the dataset description we know that there are 15 repetitions\n","    REPETITIONS = 15\n","    # Compute the duration of the recording in minutes\n","    RECORDING_DURATION = (len(signals))*(len(signals[0]))/(SAMPLING_FREQUENCY*60)\n","    # Compute number of trials\n","    TRIALS = len(word[0])\n","    # Set flag to True to balance the training set\n","    BALANCE_DATASET = False\n","\n","    print(\"**********************************\")\n","    print(\"       TRAIN SET INFORMATION      \")\n","    print(\"**********************************\")\n","    print(\"Sampling Frequency: %d Hz [%.2f ms]\" % (SAMPLING_FREQUENCY, (1000/SAMPLING_FREQUENCY)))\n","    print(\"Session duration:   %.2f min\" % RECORDING_DURATION)\n","    print(\"Number of letters:  %d\" % TRIALS)\n","    print(\"Spelled word:       %s\" % ''.join(word))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["**********************************\n","       TRAIN SET INFORMATION      \n","**********************************\n","Sampling Frequency: 240 Hz [4.17 ms]\n","Session duration:   46.01 min\n","Number of letters:  85\n","Spelled word:       VGREAAH8TVRHBYN_UGCOLO4EUERDOOHCIFOMDNU6LQCPKEIREKOYRQIDJXPBKOJDWZEUEWWFOEBHXTQTTZUMO\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"IXuVC7puKcLt","colab_type":"code","colab":{}},"source":["# Application of butterworth filter\n","b, a = signal.butter(4, [0.1/SAMPLING_FREQUENCY, 20/SAMPLING_FREQUENCY], 'bandpass')\n","for trial in range(TRIALS):\n","    signals[trial, :, :] = signal.filtfilt(b, a, signals[trial, :, :], axis=0)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"jS1FuKDuixnF","colab_type":"code","outputId":"6250822c-2e3c-47ff-8e19-c1037bb70a54","executionInfo":{"status":"ok","timestamp":1576425785805,"user_tz":-60,"elapsed":5832,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":50}},"source":["# Down-sampling of the signals from 240Hz to 120Hz\n","DOWNSAMPLING_FREQUENCY = 120\n","SCALE_FACTOR = round(SAMPLING_FREQUENCY / DOWNSAMPLING_FREQUENCY)\n","SAMPLING_FREQUENCY = DOWNSAMPLING_FREQUENCY\n","\n","print(\"# Samples of EEG signals before downsampling: {}\".format(len(signals[0])))\n","\n","signals = signals[:, 0:-1:SCALE_FACTOR, :]\n","flashing = flashing[:, 0:-1:SCALE_FACTOR]\n","stimulus = stimulus[:, 0:-1:SCALE_FACTOR]\n","\n","print(\"# Samples of EEG signals after downsampling: {}\".format(len(signals[0])))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["# Samples of EEG signals before downsampling: 7794\n","# Samples of EEG signals after downsampling: 3897\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"bGaRKwu9MIvP","colab_type":"code","outputId":"85f686e4-eaf4-4197-8374-a8e5b8d6331e","executionInfo":{"status":"ok","timestamp":1576425786791,"user_tz":-60,"elapsed":6805,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":33}},"source":["# Number of EEG channels\n","N_CHANNELS = len(CHANNELS)\n","# Window duration after each flashing [ms]\n","WINDOW_DURATION = 650\n","# Number of samples of each window\n","WINDOW_SAMPLES = round(SAMPLING_FREQUENCY * (WINDOW_DURATION / 1000))\n","# Number of samples for each character in trials\n","SAMPLES_PER_TRIAL = len(signals[0])\n","\n","train_features = []\n","train_labels = []\n","\n","count_positive = 0\n","count_negative = 0\n","\n","for trial in range(TRIALS):\n","    for sample in (range(SAMPLES_PER_TRIAL)):\n","        if (sample == 0) or (flashing[trial, sample-1] == 0 and flashing[trial, sample] == 1):\n","            lower_sample = sample\n","            upper_sample = sample + WINDOW_SAMPLES\n","            window = signals[trial, lower_sample:upper_sample, :]                \n","            # Features extraction\n","            train_features.append(window)\n","            # Labels extraction\n","            if stimulus[trial, sample] == 1:\n","                count_positive += 1\n","                train_labels.append(1) # Class P300\n","            else:\n","                count_negative += 1\n","                train_labels.append(0) # Class no-P300\n","\n","# Get negative-positive classes ratio\n","train_ratio = count_negative/count_positive\n","\n","# Convert lists to numpy arrays\n","train_features = np.array(train_features)\n","train_labels = np.array(train_labels)\n","\n","# 3D Tensor shape (SAMPLES, 78, 8)\n","dim_train = train_features.shape\n","print(\"Features tensor shape: {}\".format(dim_train))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Features tensor shape: (15300, 78, 8)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"kKMlV1Mqapew","colab_type":"code","colab":{}},"source":["# Data normalization Zi = (Xi - mu) / sigma\n","for pattern in range(len(train_features)):\n","    train_features[pattern] = scale(train_features[pattern], axis=0)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xTVrfKzJH2aE","colab_type":"text"},"source":["# Testing set processing\n","\n","1.   Application of bandpass filter **(0.1-20Hz)**;\n","2.   Down-sampling signas from 240Hz to 120Hz;\n","3.   Obtain windows of 650ms at the start of every flashing (175ms)\n","4.   Normalization of samples in each window: **Zi = (Xi - mu) / sigma**;\n","5.   Reshape each window to be a 3D tensor with dimensions: **(N_SAMPLES, 78, 8)**;\n","6.   Calculate weights vector to balance samples importance and obtain correct accuracy estimation;"]},{"cell_type":"code","metadata":{"id":"Nm-bZZsKTliz","colab_type":"code","outputId":"8b30f513-b130-4de4-8cbf-8a35263b7f67","executionInfo":{"status":"ok","timestamp":1576425793782,"user_tz":-60,"elapsed":13745,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":134}},"source":["# Test data loading\n","if not os.path.exists(SUBJECT_TEST_FILE_PATH):\n","    print(\"Missing file: {}\", SUBJECT_TEST_FILE_PATH)\n","else:\n","    # Load the required data\n","    data_test = loadmat(SUBJECT_TEST_FILE_PATH)\n","    # Get the variables of interest from the loaded dictionary\n","    signals_test = data_test['Signal']\n","    signals_test = signals_test[:, :, CHANNELS]\n","    flashing_test = data_test['Flashing']\n","    word_test =  data_test['TargetChar']\n","    stimulus_code_test = data_test['StimulusCode']\n","\n","    SAMPLING_FREQUENCY = 240\n","    # From the dataset description we know that there are 15 repetitions\n","    REPETITIONS = 15\n","    # Compute the duration of the recording in minutes\n","    RECORDING_DURATION_TEST = (len(signals_test))*(len(signals_test[0]))/(SAMPLING_FREQUENCY*60)\n","    # Compute number of trials\n","    TRIALS_TEST = len(word_test[0])\n","    # Number of samples for each character in trials\n","    SAMPLES_PER_TRIAL_TEST = len(signals_test[0])\n","    \n","    print(\"**********************************\")\n","    print(\"        TEST SET INFORMATION      \")\n","    print(\"**********************************\")\n","    print(\"Sampling Frequency: %d Hz [%.2f ms]\" % (SAMPLING_FREQUENCY, (1000/SAMPLING_FREQUENCY)))\n","    print(\"Session duration:   %.2f min\" % RECORDING_DURATION_TEST)\n","    print(\"Number of letters:  %d\" % TRIALS_TEST)\n","    print(\"Spelled word:       %s\" % ''.join(word_test))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["**********************************\n","        TEST SET INFORMATION      \n","**********************************\n","Sampling Frequency: 240 Hz [4.17 ms]\n","Session duration:   54.12 min\n","Number of letters:  100\n","Spelled word:       MERMIROOMUHJPXJOHUVLEORZP3GLOO7AUFDKEFTWEOOALZOP9ROCGZET1Y19EWX65QUYU7NAK_4YCJDVDNGQXODBEV2B5EFDIDNR\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"SvCresQJm25A","colab_type":"code","colab":{}},"source":["# Create characters matrix\n","char_matrix = [[0 for j in range(6)] for i in range(6)]\n","s = string.ascii_uppercase + '1' + '2' + '3' + '4' + '5' + '6' + '7' + '8' + '9' + '_'\n","\n","# Append cols and rows in a list\n","list_matrix = []\n","for i in range(6):\n","    col = [s[j] for j in range(i, 36, 6)]\n","    list_matrix.append(col)\n","for i in range(6):\n","    row = [s[j] for j in range(i * 6, i * 6 + 6)]\n","    list_matrix.append(row)\n","\n","# Create StimulusType array for the test set (missing from the given database)\n","stimulus_test = [[0 for j in range(SAMPLES_PER_TRIAL_TEST)] for i in range(TRIALS_TEST)]\n","stimulus_test = np.array(stimulus_test)\n","\n","for trial in range(TRIALS_TEST):\n","    counter=0\n","    for sample in range(SAMPLES_PER_TRIAL_TEST):\n","        index = int(stimulus_code_test[trial, sample]) - 1\n","        if not index == -1:\n","            if word_test[0][trial] in list_matrix[index]:\n","                stimulus_test[trial, sample] = 1\n","            else:\n","                stimulus_test[trial, sample] = 0"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"UNy5oI8ooMAw","colab_type":"code","colab":{}},"source":["# Application of butterworth filter\n","b, a = signal.butter(4, [0.1/SAMPLING_FREQUENCY, 20/SAMPLING_FREQUENCY], 'bandpass')\n","for trial in range(TRIALS_TEST):\n","    signals_test[trial, :, :] = signal.filtfilt(b, a, signals_test[trial, :, :], axis=0)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"8CpKeth5qOjr","colab_type":"code","outputId":"4ccfb558-82f2-4e0e-c79c-69e1c51fa1d2","executionInfo":{"status":"ok","timestamp":1576425794788,"user_tz":-60,"elapsed":14714,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":50}},"source":["# Down-sampling of the signals from 240Hz to 120Hz\n","DOWNSAMPLING_FREQUENCY = 120\n","SCALE_FACTOR = round(SAMPLING_FREQUENCY / DOWNSAMPLING_FREQUENCY)\n","SAMPLING_FREQUENCY = DOWNSAMPLING_FREQUENCY\n","\n","print(\"# Samples of EEG signals before downsampling: {}\".format(len(signals_test[0])))\n","\n","signals_test = signals_test[:, 0:-1:SCALE_FACTOR, :]\n","flashing_test = flashing_test[:, 0:-1:SCALE_FACTOR]\n","stimulus_test = stimulus_test[:, 0:-1:SCALE_FACTOR]\n","\n","print(\"# Samples of EEG signals after downsampling: {}\".format(len(signals_test[0])))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["# Samples of EEG signals before downsampling: 7794\n","# Samples of EEG signals after downsampling: 3897\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"48sta55OVXFy","colab_type":"code","outputId":"7f8a7ef1-12a1-4f7c-d8f4-7843f2a4ab36","executionInfo":{"status":"ok","timestamp":1576425796086,"user_tz":-60,"elapsed":15994,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":33}},"source":["# Number of EEG channels\n","N_CHANNELS = len(CHANNELS)\n","# Window duration after each flashing [ms]\n","WINDOW_DURATION = 650\n","# Number of samples of each window\n","WINDOW_SAMPLES = round(SAMPLING_FREQUENCY * (WINDOW_DURATION / 1000))\n","# Number of samples for each character in trials\n","SAMPLES_PER_TRIAL_TEST = len(signals[0])\n","\n","test_features = []\n","test_labels = []\n","windowed_stimulus = []\n","\n","count_positive = 0\n","count_negative = 0\n","\n","for trial in range(TRIALS_TEST):\n","    for sample in (range(SAMPLES_PER_TRIAL_TEST)):\n","        if (sample == 0) or (flashing_test[trial, sample-1] == 0 and flashing_test[trial, sample] == 1):\n","            lower_sample = sample\n","            upper_sample = sample + WINDOW_SAMPLES\n","            window = signals_test[trial, lower_sample:upper_sample, :]\n","            # Extracting number of row/col in a window\n","            number_stimulus = int(stimulus_code_test[trial, sample])\n","            windowed_stimulus.append(number_stimulus)\n","            # Features extraction\n","            test_features.append(window)\n","            # Labels extraction\n","            if stimulus_test[trial, sample] == 1:\n","                count_positive += 1\n","                test_labels.append(1) # Class P300\n","            else:\n","                count_negative += 1\n","                test_labels.append(0) # Class no-P300\n","\n","# Get test weights to take into account the number of classes \n","test_weights = []\n","for i in range(len(test_labels)):\n","    if test_labels[i] == 1:\n","        test_weights.append(len(test_labels)/count_positive)\n","    else:\n","        test_weights.append(len(test_labels)/count_negative)\n","test_weights = np.array(test_weights)\n","\n","# Convert lists to numpy arrays\n","test_features = np.array(test_features)\n","test_labels = np.array(test_labels)\n","\n","# 3D tensor (SAMPLES, 78, 64)\n","dim_test = test_features.shape\n","print(\"Features tensor shape: {}\".format(dim_test))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Features tensor shape: (18000, 78, 8)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"-VY8Nr04YI9F","colab_type":"code","colab":{}},"source":["# Data normalization Zi = (Xi - mu) / sigma\n","for pattern in range(len(test_features)):\n","    test_features[pattern] = scale(test_features[pattern], axis=0)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"-VK4MFXTjHoM","colab_type":"text"},"source":["# CNN2a model definition, training and testing\n","\n","1.   Function definiton for randomization of weights and biases;\n","2.   **Scaled_tanh(x)** activation function definition;\n","3.   ANN model definition (2 Conv1D layers, 2 dense layers);\n","4.   Training of the network over weighted datasets;\n","5.   CNN2a performance assessment;"]},{"cell_type":"code","metadata":{"id":"eTIc_w_TcOmK","colab_type":"code","colab":{}},"source":["# Randomizing function for bias and weights of the network\n","def cecotti_normal(shape, dtype = None, partition_info = None):\n","    if len(shape) == 1:\n","        fan_in = shape[0]\n","    elif len(shape) == 2:\n","        fan_in = shape[0]\n","    else:\n","        receptive_field_size = 1\n","        for dim in shape[:-2]:\n","            receptive_field_size *= dim\n","        fan_in = shape[-2] * receptive_field_size\n","    return K.random_normal(shape, mean = 0.0, stddev = (1.0 / fan_in))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"VeyyvqGgcTsa","colab_type":"code","colab":{}},"source":["# Custom tanh activation function\n","def scaled_tanh(z):\n","    return 1.7159 * K.tanh((2.0 / 3.0) * z)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"BuMCOgJ3ST-h","colab_type":"code","colab":{}},"source":["# Build the model\n","def CNN2a_model(channels=8, filters=10):\n","    model = Sequential([\n","        Conv1D(\n","            filters = filters,\n","            kernel_size = 1,\n","            padding = \"same\",\n","            bias_initializer = cecotti_normal,\n","            kernel_initializer = cecotti_normal,\n","            use_bias = True,\n","            activation = scaled_tanh,\n","            input_shape = (78, channels)\n","        ),\n","        Conv1D(\n","            filters = 50,\n","            kernel_size = 13,\n","            padding = \"valid\",\n","            strides = 11,\n","            bias_initializer = cecotti_normal,\n","            kernel_initializer = cecotti_normal,\n","            use_bias = True,\n","            activation = scaled_tanh,\n","        ),\n","        Flatten(),\n","        Dense(100, activation=\"sigmoid\"),\n","        Dense(1, activation=\"sigmoid\")\n","    ])\n","    model.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['accuracy'])\n","    return model"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"jVMdFs4quaZL","colab_type":"code","outputId":"add0bc5d-d0b4-492b-f0a1-c139cb6f7407","executionInfo":{"status":"ok","timestamp":1576425802839,"user_tz":-60,"elapsed":22701,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":505}},"source":["# Training parameters\n","BATCH_SIZE = 256\n","EPOCHS = 200\n","VALID_SPLIT = 0.05\n","SHUFFLE = 1 # set to 1 to shuffle subsets during training\n","\n","# Model summary\n","CNN2a_model(channels=8, filters=10).summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4409: The name tf.random_normal is deprecated. Please use tf.random.normal instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n","\n","Model: \"sequential_1\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv1d_1 (Conv1D)            (None, 78, 10)            90        \n","_________________________________________________________________\n","conv1d_2 (Conv1D)            (None, 6, 50)             6550      \n","_________________________________________________________________\n","flatten_1 (Flatten)          (None, 300)               0         \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 100)               30100     \n","_________________________________________________________________\n","dense_2 (Dense)              (None, 1)                 101       \n","=================================================================\n","Total params: 36,841\n","Trainable params: 36,841\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"GEPpgyxyTl-u","colab_type":"code","outputId":"a2fc69c4-652c-4a13-ae19-5b23e5c45ac2","executionInfo":{"status":"ok","timestamp":1576425832466,"user_tz":-60,"elapsed":52318,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# Model definition\n","model = CNN2a_model(channels=8, filters=10)\n","\n","# Callback to save best model only\n","checkpoint = ModelCheckpoint(filepath=MODEL_LOCATIONS_FILE_PATH + \"/\" + \"model\" + \".h5\", \n","                             monitor='val_loss', \n","                             mode='min',\n","                             save_best_only=True)\n","\n","# Callback to stop when loss on validation set doesn't decrease in 50 epochs\n","earlystop = EarlyStopping(monitor = 'val_loss', \n","                              mode = 'min', \n","                              patience = 50, \n","                              restore_best_weights = True)\n","\n","# Callback to keep track of model statistics\n","history = model.fit(x=train_features, \n","                    y=train_labels, \n","                    batch_size=BATCH_SIZE, \n","                    epochs=EPOCHS, \n","                    validation_split=VALID_SPLIT, \n","                    callbacks=[checkpoint, earlystop],\n","                    shuffle=SHUFFLE,\n","                    class_weight={0: 1., 1: train_ratio})"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n","\n","Train on 14535 samples, validate on 765 samples\n","Epoch 1/200\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n","\n","14535/14535 [==============================] - 3s 194us/step - loss: 0.3711 - acc: 0.6244 - val_loss: 0.3113 - val_acc: 0.6863\n","Epoch 2/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.3143 - acc: 0.7194 - val_loss: 0.2918 - val_acc: 0.7686\n","Epoch 3/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.3063 - acc: 0.7298 - val_loss: 0.2794 - val_acc: 0.7229\n","Epoch 4/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2997 - acc: 0.7453 - val_loss: 0.2738 - val_acc: 0.7294\n","Epoch 5/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2988 - acc: 0.7370 - val_loss: 0.2767 - val_acc: 0.7908\n","Epoch 6/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.2964 - acc: 0.7504 - val_loss: 0.2696 - val_acc: 0.7438\n","Epoch 7/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.2948 - acc: 0.7450 - val_loss: 0.2745 - val_acc: 0.7621\n","Epoch 8/200\n","14535/14535 [==============================] - 0s 22us/step - loss: 0.2928 - acc: 0.7437 - val_loss: 0.2732 - val_acc: 0.7765\n","Epoch 9/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2913 - acc: 0.7529 - val_loss: 0.2657 - val_acc: 0.7621\n","Epoch 10/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2904 - acc: 0.7503 - val_loss: 0.2665 - val_acc: 0.7765\n","Epoch 11/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.2897 - acc: 0.7511 - val_loss: 0.2660 - val_acc: 0.7595\n","Epoch 12/200\n","14535/14535 [==============================] - 0s 22us/step - loss: 0.2894 - acc: 0.7529 - val_loss: 0.2612 - val_acc: 0.7817\n","Epoch 13/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.2893 - acc: 0.7514 - val_loss: 0.2598 - val_acc: 0.7752\n","Epoch 14/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2888 - acc: 0.7516 - val_loss: 0.2633 - val_acc: 0.7895\n","Epoch 15/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2879 - acc: 0.7509 - val_loss: 0.2621 - val_acc: 0.7699\n","Epoch 16/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2869 - acc: 0.7512 - val_loss: 0.2638 - val_acc: 0.7412\n","Epoch 17/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2864 - acc: 0.7517 - val_loss: 0.2610 - val_acc: 0.7529\n","Epoch 18/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2854 - acc: 0.7579 - val_loss: 0.2585 - val_acc: 0.7412\n","Epoch 19/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.2862 - acc: 0.7515 - val_loss: 0.2587 - val_acc: 0.7712\n","Epoch 20/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2846 - acc: 0.7529 - val_loss: 0.2580 - val_acc: 0.7556\n","Epoch 21/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2845 - acc: 0.7552 - val_loss: 0.2546 - val_acc: 0.7778\n","Epoch 22/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2835 - acc: 0.7520 - val_loss: 0.2587 - val_acc: 0.7203\n","Epoch 23/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2825 - acc: 0.7569 - val_loss: 0.2545 - val_acc: 0.7686\n","Epoch 24/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2815 - acc: 0.7564 - val_loss: 0.2617 - val_acc: 0.7451\n","Epoch 25/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.2818 - acc: 0.7534 - val_loss: 0.2534 - val_acc: 0.7686\n","Epoch 26/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2790 - acc: 0.7573 - val_loss: 0.2537 - val_acc: 0.7386\n","Epoch 27/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2786 - acc: 0.7602 - val_loss: 0.2539 - val_acc: 0.7320\n","Epoch 28/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.2779 - acc: 0.7621 - val_loss: 0.2557 - val_acc: 0.7242\n","Epoch 29/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2773 - acc: 0.7581 - val_loss: 0.2512 - val_acc: 0.7438\n","Epoch 30/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2763 - acc: 0.7614 - val_loss: 0.2511 - val_acc: 0.7843\n","Epoch 31/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2745 - acc: 0.7609 - val_loss: 0.2492 - val_acc: 0.7673\n","Epoch 32/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2728 - acc: 0.7637 - val_loss: 0.2511 - val_acc: 0.7869\n","Epoch 33/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2705 - acc: 0.7697 - val_loss: 0.2506 - val_acc: 0.7346\n","Epoch 34/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2693 - acc: 0.7680 - val_loss: 0.2510 - val_acc: 0.7556\n","Epoch 35/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2677 - acc: 0.7699 - val_loss: 0.2512 - val_acc: 0.7621\n","Epoch 36/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.2652 - acc: 0.7706 - val_loss: 0.2492 - val_acc: 0.7817\n","Epoch 37/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2641 - acc: 0.7734 - val_loss: 0.2539 - val_acc: 0.7516\n","Epoch 38/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2619 - acc: 0.7781 - val_loss: 0.2471 - val_acc: 0.7712\n","Epoch 39/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.2607 - acc: 0.7755 - val_loss: 0.2476 - val_acc: 0.8013\n","Epoch 40/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.2596 - acc: 0.7778 - val_loss: 0.2477 - val_acc: 0.7634\n","Epoch 41/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2565 - acc: 0.7829 - val_loss: 0.2499 - val_acc: 0.7791\n","Epoch 42/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2540 - acc: 0.7828 - val_loss: 0.2505 - val_acc: 0.7660\n","Epoch 43/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.2510 - acc: 0.7856 - val_loss: 0.2490 - val_acc: 0.8039\n","Epoch 44/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.2488 - acc: 0.7889 - val_loss: 0.2507 - val_acc: 0.7621\n","Epoch 45/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2454 - acc: 0.7917 - val_loss: 0.2475 - val_acc: 0.7739\n","Epoch 46/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2436 - acc: 0.7910 - val_loss: 0.2498 - val_acc: 0.7595\n","Epoch 47/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2400 - acc: 0.7997 - val_loss: 0.2443 - val_acc: 0.7948\n","Epoch 48/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2378 - acc: 0.7977 - val_loss: 0.2488 - val_acc: 0.7765\n","Epoch 49/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2331 - acc: 0.8052 - val_loss: 0.2551 - val_acc: 0.7961\n","Epoch 50/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.2301 - acc: 0.8055 - val_loss: 0.2522 - val_acc: 0.7739\n","Epoch 51/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2261 - acc: 0.8077 - val_loss: 0.2515 - val_acc: 0.7634\n","Epoch 52/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2225 - acc: 0.8125 - val_loss: 0.2563 - val_acc: 0.8118\n","Epoch 53/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.2195 - acc: 0.8165 - val_loss: 0.2518 - val_acc: 0.7556\n","Epoch 54/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2142 - acc: 0.8200 - val_loss: 0.2538 - val_acc: 0.8000\n","Epoch 55/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.2094 - acc: 0.8288 - val_loss: 0.2524 - val_acc: 0.7895\n","Epoch 56/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2060 - acc: 0.8282 - val_loss: 0.2536 - val_acc: 0.7935\n","Epoch 57/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.2014 - acc: 0.8338 - val_loss: 0.2572 - val_acc: 0.7817\n","Epoch 58/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.1970 - acc: 0.8375 - val_loss: 0.2608 - val_acc: 0.8196\n","Epoch 59/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.1927 - acc: 0.8455 - val_loss: 0.2514 - val_acc: 0.7725\n","Epoch 60/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1872 - acc: 0.8480 - val_loss: 0.2582 - val_acc: 0.8131\n","Epoch 61/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1842 - acc: 0.8533 - val_loss: 0.2601 - val_acc: 0.7686\n","Epoch 62/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.1787 - acc: 0.8544 - val_loss: 0.2631 - val_acc: 0.7948\n","Epoch 63/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.1728 - acc: 0.8631 - val_loss: 0.2588 - val_acc: 0.8065\n","Epoch 64/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.1697 - acc: 0.8658 - val_loss: 0.2643 - val_acc: 0.8131\n","Epoch 65/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1650 - acc: 0.8692 - val_loss: 0.2567 - val_acc: 0.7856\n","Epoch 66/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.1603 - acc: 0.8746 - val_loss: 0.2595 - val_acc: 0.7843\n","Epoch 67/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1551 - acc: 0.8796 - val_loss: 0.2722 - val_acc: 0.8209\n","Epoch 68/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.1506 - acc: 0.8852 - val_loss: 0.2632 - val_acc: 0.8000\n","Epoch 69/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.1461 - acc: 0.8887 - val_loss: 0.2667 - val_acc: 0.7935\n","Epoch 70/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.1428 - acc: 0.8914 - val_loss: 0.2668 - val_acc: 0.8052\n","Epoch 71/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.1367 - acc: 0.8963 - val_loss: 0.2638 - val_acc: 0.8000\n","Epoch 72/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.1328 - acc: 0.9015 - val_loss: 0.2652 - val_acc: 0.8013\n","Epoch 73/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1293 - acc: 0.9028 - val_loss: 0.2701 - val_acc: 0.8092\n","Epoch 74/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.1259 - acc: 0.9072 - val_loss: 0.2698 - val_acc: 0.7974\n","Epoch 75/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1205 - acc: 0.9098 - val_loss: 0.2737 - val_acc: 0.8039\n","Epoch 76/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.1173 - acc: 0.9143 - val_loss: 0.2755 - val_acc: 0.8196\n","Epoch 77/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1133 - acc: 0.9184 - val_loss: 0.2787 - val_acc: 0.8144\n","Epoch 78/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.1087 - acc: 0.9224 - val_loss: 0.2779 - val_acc: 0.8105\n","Epoch 79/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1048 - acc: 0.9244 - val_loss: 0.2882 - val_acc: 0.8248\n","Epoch 80/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.1013 - acc: 0.9285 - val_loss: 0.2804 - val_acc: 0.8118\n","Epoch 81/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.0978 - acc: 0.9320 - val_loss: 0.2876 - val_acc: 0.8144\n","Epoch 82/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.0946 - acc: 0.9338 - val_loss: 0.2878 - val_acc: 0.8222\n","Epoch 83/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.0911 - acc: 0.9368 - val_loss: 0.2839 - val_acc: 0.8196\n","Epoch 84/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.0876 - acc: 0.9405 - val_loss: 0.2926 - val_acc: 0.8235\n","Epoch 85/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.0853 - acc: 0.9414 - val_loss: 0.2918 - val_acc: 0.8288\n","Epoch 86/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.0818 - acc: 0.9451 - val_loss: 0.2940 - val_acc: 0.8275\n","Epoch 87/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.0791 - acc: 0.9477 - val_loss: 0.2941 - val_acc: 0.8209\n","Epoch 88/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.0764 - acc: 0.9498 - val_loss: 0.2972 - val_acc: 0.8314\n","Epoch 89/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.0735 - acc: 0.9513 - val_loss: 0.3164 - val_acc: 0.8392\n","Epoch 90/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.0715 - acc: 0.9547 - val_loss: 0.3007 - val_acc: 0.8340\n","Epoch 91/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.0686 - acc: 0.9567 - val_loss: 0.3058 - val_acc: 0.8327\n","Epoch 92/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.0657 - acc: 0.9586 - val_loss: 0.3076 - val_acc: 0.8288\n","Epoch 93/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.0631 - acc: 0.9624 - val_loss: 0.3147 - val_acc: 0.8340\n","Epoch 94/200\n","14535/14535 [==============================] - 0s 20us/step - loss: 0.0613 - acc: 0.9612 - val_loss: 0.3138 - val_acc: 0.8379\n","Epoch 95/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.0591 - acc: 0.9641 - val_loss: 0.3104 - val_acc: 0.8314\n","Epoch 96/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.0571 - acc: 0.9659 - val_loss: 0.3208 - val_acc: 0.8405\n","Epoch 97/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.0548 - acc: 0.9680 - val_loss: 0.3196 - val_acc: 0.8392\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"eJHaj2f1VFYZ","colab_type":"code","outputId":"c218f69b-ffc9-4e6c-f66e-cdf013e4c2a2","executionInfo":{"status":"ok","timestamp":1576425833149,"user_tz":-60,"elapsed":52981,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":265}},"source":["plt.figure(figsize=(15,4))\n","plt.style.use('tableau-colorblind10')\n","\n","# Plots of loss curves during training\n","plt.subplot(1,2,1)\n","plt.plot(history.history['loss'], label=\"training loss\")\n","plt.plot(history.history['val_loss'], label=\"validation loss\")\n","plt.legend()\n","\n","# Plots of accuracy curves during training\n","plt.subplot(1,2,2)\n","plt.plot(history.history['acc'], label=\"training accuracy\")\n","plt.plot(history.history['val_acc'], label=\"validation accuracy\")\n","plt.legend()\n","\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA3cAAAD4CAYAAABPPR+GAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdd1yW5f7A8c/NXoKIi6HiFlmCOHLk\n3uXMkVnqSa1O5qlOnuxk2jidlqf8VTa0tJ2auXOkpaHlANwbB8pysZEN1++PCxEHggo8jO/79eIF\nz72e7/3AzfN87+u6vpehlEIIIYQQQgghROVmZuoAhBBCCCGEEELcO0nuhBBCCCGEEKIKkOROCCGE\nEEIIIaoASe6EEEIIIYQQogqQ5E4IIYQQQgghqgALUwdwo9q1aytPT09ThyGEEKIchIWFXVZK1TF1\nHJWFvEcKIUT1cLfvjxUuufP09CQ0NNTUYQghhCgHhmGcNXUMlYm8RwohRPVwt++P0i1TCCGEEEII\nIaoASe6EEEIIIYQQogqQ5E4IIYQQQgghqoAKN+ZOCCFKKjs7m6ioKDIyMkwdiiiGjY0NHh4eWFpa\nmjqUKkeuA3Ejud6EqL4kuRNCVFpRUVHUqFEDT09PDMMwdTiiCEop4uLiiIqKonHjxqYOp8qR60AU\nJtebENWbdMsUQlRaGRkZuLi4yAfaCs4wDFxcXKRlqYzIdSAKk+tNiOpNkjshRKUmH2grB/k9lS15\nfUVh8vcgRPVV5ZK7zOxc3lh/kK0nLpg6FCGEEEIIIUQ1cSUzh5/2nOXdTYdNFkOVS+4szc14ff1B\nfj0Wa+pQhBBVXGJiIp988sld7Ttw4EASExNvu82sWbPYvHnzXR3/Rp6enly+fLlUjiVEYZXpOhBC\niNKWmJbFj6ERjFgQTJ0Zyxi1cDsfB58gOzfPJPFUuYIqZmYGrk62xCSlmzoUIUQVd/VD7d///veb\n1uXk5GBhUfS/2HXr1hV7/Ndff/2e4hOiPMh1cLPizlsIUblFxKWyfF8kaw5Fs+3URXLzFPUdbfjb\nfU15qE1Dujarg7mZadrQqlzLHYCbky2xktwJIcrYjBkzOHXqFG3atGH69Ols3bqVrl27MnjwYFq3\nbg3A0KFDadu2Ld7e3syfP79g36staREREXh5eTF58mS8vb3p27cv6en6/9eECRNYtmxZwfazZ88m\nMDAQX19fjh07BsClS5fo06cP3t7eTJo0iUaNGhXbQvf+++/j4+ODj48Pc+fOBeDKlSsMGjQIf39/\nfHx8WLJkScE5tm7dGj8/P1544YXSfQFFlVCZroOnnnqKoKAgvL29mT17dsHykJAQOnXqhL+/P+3b\ntyclJYXc3FxeeOEFfHx88PPz46OPProuZoDQ0FC6d+8OwKuvvsqjjz5K586defTRR4mIiKBr164E\nBgYSGBjIX3/9VfB877zzDr6+vvj7+xe8foGBgQXrw8PDr3sshCh/xy8kc/pyCkqpgmVh5+IYs3A7\nTV9dzT9X7CHuSib/6t2a7c/1Ieo/w/h4VDu6t6hnssQOqmDLHYCbkx3hF5NNHYYQohw9uyyUfVEJ\npXrMNh7OzH0oqMj1b7/9NocOHWLfvn0AbN26lT179nDo0KGCEuQLFy6kVq1apKen065dO0aMGIGL\ni8t1xwkPD+fHH39kwYIFjBo1ip9//plx48bd9Hy1a9dmz549fPLJJ8yZM4cvvviC1157jZ49e/LS\nSy+xYcMGvvzyy9ueU1hYGIsWLWLXrl0opejQoQPdunXj9OnTuLm58csvvwCQlJREXFwcK1as4Nix\nYxiGUWz3OWF6ch3c/jp48803qVWrFrm5ufTq1YsDBw7QqlUrRo8ezZIlS2jXrh3JycnY2toyf/58\nIiIi2LdvHxYWFsTHxxf7Wh05coTt27dja2tLWloamzZtwsbGhvDwcB5++GFCQ0NZv349q1atYteu\nXdjZ2REfH0+tWrVwcnJi3759tGnThkWLFjFx4sRin08IUfrOJ6czfcUevguJAMDRxhJ/95oYhkHw\nyYs42lgyvbcXT3ZpjqeLg2mDvYUq23In3TKFEKbQvn376+aW+vDDD/H396djx45ERkYSHh5+0z6N\nGzemTZs2ALRt25aIiIhbHnv48OE3bbN9+3bGjBkDQP/+/XF2dr5tfNu3b2fYsGHY29vj4ODA8OHD\n2bZtG76+vmzatIkXX3yRbdu24eTkhJOTEzY2Njz++OMsX74cOzu7O305RDVVUa+DpUuXEhgYSEBA\nAIcPH+bIkSMcP34cV1dX2rVrB4CjoyMWFhZs3ryZJ554oqB7Za1atYo978GDB2NrawvoyeUnT56M\nr68vI0eO5MiRIwBs3ryZiRMnFlxPV487adIkFi1aRG5uLkuWLGHs2LHFPp8QovTk5Obx4dZjtHx9\nDUv3nuPffb1ZMLYDj7ZvTJ6CiykZvDs0gMg3hvH2kIAKmdhBlW25syU+LYuM7FxsLM1NHY4Qohzc\nrmWhPNnb2xf8vHXrVjZv3syOHTuws7Oje/fut5x7ytrauuBnc3Pzgu5oRW1nbm5OTk5OqcbdokUL\n9uzZw7p165g5cya9evVi1qxZ7N69m99++41ly5bx8ccf8/vvv5fq84rSJddB0c6cOcOcOXMICQnB\n2dmZCRMm3NVccBYWFuTl6UIJN+5f+Lw/+OAD6tWrx/79+8nLy8PGxua2xx0xYkRBC2Tbtm1vatkU\nQpS+pPQsNh6N5ZdD0aw7EsPl1Ez6tnLlo5FBtKjnaOrw7kqVbbkDZNydEKJM1ahRg5SUlCLXJyUl\n4ezsjJ2dHceOHWPnzp2lHkPnzp1ZunQpAL/++isJCbfvkte1a1dWrlxJWloaV65cYcWKFXTt2pWY\nmBjs7OwYN24c06dPZ8+ePaSmppKUlMTAgQP54IMP2L9/f6nHLyq/ynIdJCcnY29vj5OTExcuXGD9\n+vUAtGzZktjYWEJCQgBISUkhJyeHPn368PnnnxckkFe7ZXp6ehIWFgbAzz//XGRMSUlJuLq6YmZm\nxrfffktubi4Affr0YdGiRaSlpV13XBsbG/r168dTTz0lXTKFKENxqZks3HGKAZ/8Tu0XlzF64XbW\nHoqmv5cra57oxoane1TaxA6qbMud7uoQk5RG49oVs8lUCFH5ubi40LlzZ3x8fBgwYACDBg26bn3/\n/v357LPP8PLyomXLlnTs2LHUY5g9ezYPP/ww3377Lffddx/169enRo0aRW4fGBjIhAkTaN++PaC7\nggUEBLBx40amT5+OmZkZlpaWfPrpp6SkpDBkyBAyMjJQSvH++++XevyVnWEY/YH/A8yBL5RSb9+w\nvhGwEKgDxAPjlFJR+etygYP5m55TSg0ut8BLUWW5Dvz9/QkICKBVq1Y0aNCAzp07A2BlZcWSJUt4\n5plnSE9Px9bWls2bNzNp0iROnDiBn58flpaWTJ48malTpzJ79mwef/xxXnnllYJiKrfy97//nREj\nRvDNN9/Qv3//gla9/v37s2/fPoKCgrCysmLgwIH897//BeCRRx5hxYoV9O3bt9RfIyGqu0Mxibyy\ndj9rDkWTm6do7OLAcz1bMcTXg46Na5u0CEppMgpXgKkIgoKCVGho6D0d41BMIr7//YWlf+vCyMBG\npRSZEKKiOXr0KF5eXqYOw6QyMzMxNzfHwsKCHTt28NRTTxUUtqhobvX7MgwjTClVMfoS3iHDMMyB\nE0AfIAoIAR5WSh0ptM1PwFql1NeGYfQEJiqlHs1fl6qUuqM7kLd6j5TroHJdB7czZ84ckpKSeOON\nN+75WPJ3IYR2Lv4Ks385wNe7T+NoY8mTXZozKrARAR7OGIZh6vCKdLfvj1W05U53y5SiKkKIqu7c\nuXOMGjWKvLw8rKysWLBggalDqk7aAyeVUqcBDMNYDAwBjhTapjXwfP7PW4CV5RphNVEVroNhw4Zx\n6tQpGdcqxD3Kyskl9Fw8wScvEnzyIr+fOI8Cnu/hxUt9vXFxsC72GJVZiZK7EnQ7eRJ4GsgFUoEp\nSqkjhmF4AkeB4/mb7lRKPVk6oRfN2c4KawszSe6EEFVe8+bN2bt3r6nDqK7cgchCj6OADjdssx8Y\njn4PHQbUMAzDRSkVB9gYhhEK5ABvK6Uk8btLVeE6WLFihalDEKLSSsvKYf3hGJbtO8faQ9GkZuqx\nsq3rOzGlc3P+2asVjWpVj6FaxSZ3+d1O5lGo24lhGKsLdzsBflBKfZa//WDgfaB//rpTSqk2pRt2\nsTHj5mRHTFJaeT6tEEIIcaMXgI8Nw5gABAPR6BuhAI2UUtGGYTQBfjcM46BS6tSNBzAMYwowBaBh\nw4blE7UQQlRwSil2n43jk+ATLNt3jrSsXGo7WDM2yJP+rd3o0qQOdWrcvkptVVSSlrtiu50opQrP\nGG4PmHwgn8x1J4QQooxFAw0KPfbIX1ZAKRWDbrnDMAwHYIRSKjF/XXT+99OGYWwFAoCbkjul1Hxg\nPugxd6V+FkIIUYnk5Obx7e4zfBx8gj2R8ThYWzCuXWNGBzbi/mZ1sTCvGoVR7lZJkruSdDvBMIyn\n0eMKrICehVY1NgxjL5AMzFRKbbvFvqV+V9LNyZaDMYmlciwhhBDiFkKA5oZhNEYndWOA62aeNgyj\nNhCvlMoDXkJXzsQwDGcgTSmVmb9NZ+Dd8gxeCCEqm60nLvDMTyEcik2idX0n5o1qx6PtG1PDxtLU\noVUYpVZQRSk1D5hnGMZYYCYwHogFGiq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AEKCAsPx9E8r3LIQQ1UViWha/Hosl5Gwcu8/G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6CI99Ye4M01+ziSlkOHJv68dmUUV/QMp21jk+l65PBp/u+rjVzxwe+M8YljhT80b9uT/ddMoU1I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\nF3dmZmZmZmY18A8hlX6TwlLDRQAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 1080x288 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"Sg1zrX0xZTRt","colab_type":"code","colab":{}},"source":["# Define model\n","best_model = CNN2a_model(channels=8, filters=10)\n","\n","# Load best model weights from h5 file\n","best_model.load_weights(MODEL_LOCATIONS_FILE_PATH + \"/model.h5\")\n","\n","# Compile best model model\n","best_model.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['accuracy'])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"U9-pnknkO9PN","colab_type":"code","outputId":"e58dfabf-3f94-460c-a03b-58c1b0b087a7","executionInfo":{"status":"ok","timestamp":1576425835503,"user_tz":-60,"elapsed":55312,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":377}},"source":["# Load topoplot coordinates\n","if not os.path.exists(CHANNEL_COORD):\n","    print(\"Missing file: {}\".format(CHANNEL_COORD))\n","else:\n","    xycoord = []\n","    # Read file content\n","    with open(CHANNEL_COORD, \"r\") as f:\n","        file_content = f.read()\n","        # Loop over all rows\n","        for row in file_content.split(\"\\n\"): \n","        # Skip missing rows\n","            if row == '':\n","                continue\n","            xycoord.append(row)\n","\n","# Create a list with x,y coordinates of each electrodes well formatted\n","coord = []\n","for x in xycoord:\n","    coord.append(x.split(','))\n","coord = np.array(coord)\n","\n","# Flip y axis in order to plot in the right way the electrodes positions\n","for i in range(len(coord)):\n","    coord[i][1] = 681 -int(coord[i][1])\n","\n","# Create a list of weights for all filters\n","nf = []\n","for filt in range(10):\n","    nf.append(abs(np.array(best_model.get_weights()[0])[0, :, filt]))\n","nf = np.array(nf, dtype=float)\n","\n","# Add -1 to missing channels values\n","tmp_full = []\n","for i in range(10):\n","    tmp = np.full(64, 0, dtype=float)\n","    for c in range(N_CHANNELS):\n","        tmp[CHANNELS[c]] = nf[i][c]\n","    tmp_full.append(tmp)\n","nf = np.array(tmp_full, dtype=float)\n","\n","# Plot topoplot of the 10 filters in the first convolutional layer\n","fig = plt.figure(figsize=(15,6))\n","for i in range(10):\n","    ax = fig.add_subplot(2,5,i+1)\n","    ax.set_title(\"Filter #{}\".format(i+1))\n","    mne.viz.plot_topomap(nf[i], coord, cmap='Spectral_r', axes=ax, show=False)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA1MAAAFoCAYAAACymqHbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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/6B1sw6O7E6bZ9ONb0YtI6KwgizKYMvP727xFUE/fXnVd+KarLLkmYY/D2kccwv/LKmM00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ipbjbd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i18uWxAtyyqyrFyjrMj5GSlV9aKpHlSl+DUO+tuzuv9eBVyyD6JLsJrEjqvEVmX+8NRZ/vBn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vrihzbPsLuZ3bwJ1d28bsXruLLf3If0vhBemtCXNnh0t6TRe9Yy3Tez3pW7EAti7RochBD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\nEAgEAucgCFOBQCAQCAQCgUAgcA7+G9t5YgZ1bhDsAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 1080x432 with 10 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"EzbVkDAwUmfa","colab_type":"code","outputId":"6af973b1-0600-4db6-e37b-c7134d956121","executionInfo":{"status":"ok","timestamp":1576425836443,"user_tz":-60,"elapsed":56237,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":84}},"source":["# Accuracy over the testing set\n","predictions = best_model.predict(test_features)\n","predictions = np.round(predictions)\n","\n","score = np.array(best_model.evaluate(test_features, test_labels, verbose=0, sample_weight=test_weights))\n","print(\"Model performance on test set:\\t[ Loss: {}\\tAccuracy: {} ]\".format(*score.round(4)))\n","print(\"\\nPredictions: {}\\nSolutions:   {}\".format(list(map(int, predictions))[:50], list(map(int, test_labels))[:50]))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Model performance on test set:\t[ Loss: 0.4087\tAccuracy: 0.762 ]\n","\n","Predictions: [0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1]\n","Solutions:   [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"DVKxqyKZpyg8","colab_type":"code","outputId":"9ec2f454-69ad-4640-dadf-cd855dea7781","executionInfo":{"status":"ok","timestamp":1576425836873,"user_tz":-60,"elapsed":56651,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":392}},"source":["# Weighted confusion matrix (noP300: 80%, P300: 20%)\n","data = confusion_matrix(y_true=test_labels, y_pred=predictions, sample_weight=test_weights)\n","\n","# Normalized confusion matrix (values in range 0-1)\n","data_norm = data/np.full(data.shape, len(test_labels))\n","\n","# Plot the confusion matrix\n","df_cm = pd.DataFrame(data_norm, columns=np.unique(test_labels), index = np.unique(test_labels))\n","df_cm.index.name = 'Actual'\n","df_cm.columns.name = 'Predicted'\n","plt.figure(figsize = (6,5))\n","sns.set(font_scale = 1.4)\n","cm = sns.heatmap(df_cm, cmap=\"Blues\", annot=True, annot_kws = {\"size\": 16}, vmin=0, vmax=1)\n","cm.axes.set_title(\"CNN2a confusion matrix\\n\", fontsize=20)\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYgAAAF3CAYAAAC/h9zqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJAoulxOW6pgK/Njds06SAwUIEZHkaiGDcPcZQNYLufsgYFDM9vsJ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qthVDuwNXEJoZhIR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\nxFKAEBGRWAoQIiISSwFCRERi/T/C9dHqf7JKFAAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x360 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"CkdtywrVpz0r","colab_type":"code","colab":{}},"source":["# Model metrics (sens, spec, ppv, npv)\n","def model_metrics(conf_matrix):\n","    tn, fp, fn, tp = list(data_norm.flatten())\n","    sens = round(tp/(tp+fn),4) # Sensitivity\n","    spec = round(tn/(tn+fp),4) # Specificity\n","    ppv = round(tp/(tp+fp),4) # Positive Predicted Value\n","    npv = round(tn/(tn+fn),4) # Negative Predicted Value\n","    return {\"Sensitivity\":sens, \"Specificity\":spec, \"PPV\":ppv, \"NPV\":npv}"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"4zpRP7GZp0B-","colab_type":"code","outputId":"88ca45b9-9fee-4d9f-bc04-2de7a2657415","executionInfo":{"status":"ok","timestamp":1576425836875,"user_tz":-60,"elapsed":56622,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":85}},"source":["# Put model metrics into a table\n","metrics = model_metrics(data_norm)\n","\n","# Create figure\n","fig = plt.figure(figsize=(5,1))\n","ax = fig.add_subplot(111)\n","\n","# Hide graph outlines\n","for item in [fig, ax]:\n","    item.patch.set_visible(False)\n","ax.xaxis.set_visible(False)\n","ax.yaxis.set_visible(False)\n","ax.spines[\"top\"].set_visible(False)\n","ax.spines[\"bottom\"].set_visible(False)\n","ax.spines[\"left\"].set_visible(False)\n","ax.spines[\"right\"].set_visible(False)\n","\n","# Table definition\n","table = ax.table(cellText=[list(metrics.values())], \n","                     colLabels=list(metrics.keys()),\n","                     loc=\"center\",\n","                     cellLoc=\"center\",\n","                     colColours=[\"c\"]*4)\n","table.set_fontsize(16)\n","table.scale(2,2)\n","\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjwAAABECAYAAACF4e8fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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size 360x72 with 1 Axes>"]},"metadata":{"tags":[]}}]}]}